TAILIEUCHUNG - Báo cáo khoa học: "Cube Summing, Approximate Inference with Non-Local Features, and Dynamic Programming without Semirings"

We introduce cube summing, a technique that permits dynamic programming algorithms for summing over structures (like the forward and inside algorithms) to be extended with non-local features that violate the classical structural independence assumptions. It is inspired by cube pruning (Chiang, 2007; Huang and Chiang, 2007) in its computation of non-local features dynamically using scored k-best lists, but also maintains additional residual quantities used in calculating approximate marginals. . | Cube Summing Approximate Inference with Non-Local Features and Dynamic Programming without Semirings Kevin Gimpel and Noah A. Smith Language Technologies Institute Carnegie Mellon University PittsbUrgh PA 15213 UsA kgimpel nasmith @ Abstract We introduce cube summing a technique that permits dynamic programming algorithms for summing over structures like the forward and inside algorithms to be extended with non-local features that violate the classical structural independence assumptions. It is inspired by cube pruning Chiang 2007 Huang and Chiang 2007 in its computation of non-local features dynamically using scored k-best lists but also maintains additional residual quantities used in calculating approximate marginals. When restricted to local features cube summing reduces to a novel semiring k-best residual that generalizes many of the semirings of Goodman 1999 . When non-local features are included cube summing does not reduce to any semiring but is compatible with generic techniques for solving dynamic programming equations. 1 Introduction Probabilistic NLP researchers frequently make independence assumptions to keep inference algorithms tractable. Doing so limits the features that are available to our models requiring features to be structurally local. Yet many problems in NLP machine translation parsing named-entity recognition and others have benefited from the addition of non-local features that break classical independence assumptions. Doing so has required algorithms for approximate inference. Recently cube pruning Chiang 2007 Huang and Chiang 2007 was proposed as a way to leverage existing dynamic programming algorithms that find optimal-scoring derivations or structures when only local features are involved. Cube pruning permits approximate decoding with non-local features but leaves open the question of how the feature weights or probabilities are learned. Meanwhile some learning algorithms like maximum likelihood for conditional log-linear

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